by Reverse causal reasoning and inference to the best explanation One of the few statisticians that yours truly has on his blogroll is Andrew Gelman. Although not sharing his Bayesian leanings, I find his thought-provoking and non-dogmatic statistical thinking highly recommendable. The plaidoyer infra for “reverse causal questioning” is typical Gelmanian: When statistical and econometrc methodologists write about causal inference, they generally focus on forward causal questions. We are taught to answer questions of the type “What if?”, rather than “Why?” Following the work by Rubin (1977) causal questions are typically framed in terms of manipulations: if x were changed by one unit, how much would y be expected to change? But reverse causal questions are
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Reverse causal reasoning and inference to the best explanation
One of the few statisticians that yours truly has on his blogroll is Andrew Gelman. Although not sharing his Bayesian leanings, I find his thought-provoking and non-dogmatic statistical thinking highly recommendable. The plaidoyer infra for “reverse causal questioning” is typical Gelmanian:
When statistical and econometrc methodologists write about causal inference, they generally focus on forward causal questions. We are taught to answer questions of the type “What if?”, rather than “Why?” Following the work by Rubin (1977) causal questions are typically framed in terms of manipulations: if x were changed by one unit, how much would y be expected to change? But reverse causal questions are important too … In many ways, it is the reverse causal questions that motivate the research, including experiments and observational studies, that we use to answer the forward questions …
Reverse causal reasoning is different; it involves asking questions and searching for new variables that might not yet even be in our model. We can frame reverse causal questions as model checking. It goes like this: what we see is some pattern in the world that needs an explanation. What does it mean to “need an explanation”? It means that existing explanations — the existing model of the phenomenon — does not do the job …
By formalizing reverse casual reasoning within the process of data analysis, we hope to make a step toward connecting our statistical reasoning to the ways that we naturally think and talk about causality. This is consistent with views such as Cartwright (2007) that causal inference in reality is more complex than is captured in any theory of inference … What we are really suggesting is a way of talking about reverse causal questions in a way that is complementary to, rather than outside of, the mainstream formalisms of statistics and econometrics.
In a time when scientific relativism is expanding, it is important to keep up the claim for not reducing science to a pure discursive level. We have to maintain the Enlightenment tradition of thinking of reality as something more and beyond our theories and concepts of it — and of the main task of science as studying the structure of this reality.
Science is made possible by the fact that there exists a reality beyond our theories and concepts of it. It is this reality that our theories in some way deal with. Contrary to positivism, I would as a critical realist argue that the main task of science is not to detect event-regularities between observed facts. Rather, that task should be conceived as identifying the underlying structure and forces that produce the observed events.
In Gelman’s essay there is no explicit argument for abduction — inference to the best explanation — but I would still argue that it is de facto nothing but a very strong argument for why scientific realism and inference to the best explanation are the best alternatives for explaining what is going on in the world we live in. The focus on causality, model checking, anomalies and context-dependence is as close to abductive reasoning as we get in statistics and econometrics today.